A SMART PREDICTIVE MAINTENANCE ARCHITECTURE FOR INDUSTRIAL EQUIPMENT MONITORING USING IIOT, MACHINE LEARNING, AND DIGITAL TWIN MODELS
AUTHORS:
Mentor
Dr B Ramprasad
Affiliation
Department Of ECE, SVS Group of Institutions, Hanmakonda, Telangana
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Abstract
The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing by enabling intelligent monitoring and automation of industrial equipment. However, unexpected machine failures continue to cause production downtime, increased maintenance costs, and reduced operational efficiency. Predictive maintenance has emerged as an effective strategy to address these challenges by forecasting equipment failures before they occur. This paper proposes an Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology. The proposed framework continuously collects machine parameters such as vibration, temperature, pressure, current consumption, and acoustic signals through connected sensors. The collected data is analyzed using machine learning algorithms to identify anomalies, estimate remaining useful life (RUL), and generate maintenance recommendations. A digital twin model provides a virtual representation of industrial assets, enabling real-time simulation and performance evaluation. Experimental results demonstrate significant improvements in fault detection accuracy, equipment availability, and maintenance efficiency while reducing downtime and operational expenses. The proposed system contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Keywords
Industrial Internet of Things (IIoT) Predictive Maintenance Industry 4.0 Machine Learning Digital Twin Edge Computing Smart Manufacturing Condition Monitoring.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 7
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Jul 10 2026
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Sri, B. D. & Prasad, G. (2026). A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models. International Journal of Science, Strategic Management and Technology, 02(7). https://doi.org/10.55041/ijsmt.v2i7.028
Sri, Bolloju, and Gajula Prasad. "A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models." International Journal of Science, Strategic Management and Technology, vol. 02, no. 7, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i7.028.
Sri, Bolloju, and Gajula Prasad. "A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models." International Journal of Science, Strategic Management and Technology 02, no. 7 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i7.028.
References
[1] L. Da Xu, W. He, and S. Li, “Internet of Things in Industries: A Survey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, 2014.
[2] J. Lee, B. Bagheri, and H. A. Kao, “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.
[3] S. Yin and O. Kaynak, “Big Data for Modern Industry: Challenges and Trends,” Proceedings of the IEEE, vol. 103, no. 2, pp. 143–146, 2015.
[4] A. Carvalho, E. N. S. Ferreira, and P. P. C. C. Nunes, “Machine Learning Methods Applied to Predictive Maintenance,” Computers & Industrial Engineering, vol. 137, pp. 106024, 2019.
[5] F. Tao and M. Zhang, “Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing,” IEEE Access, vol. 5, pp. 20418–20427, 2017.
[6] S. Wan, J. Lu, and P. Fan, “Edge Computing for Industrial IoT Applications,” IEEE Network, vol. 32, no. 1, pp. 58–65, 2018.
[7] A. Kusiak, “Smart Manufacturing and Predictive Maintenance: Future Trends,” Journal of Manufacturing Systems, vol. 49, pp. 136–145, 2018.
[8] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable and Undesirable Emergent Behavior in Complex Systems,” Springer, 2017.
[9] J. Chen, Y. Wang, and X. Li, “Artificial Intelligence Driven Predictive Maintenance for Industrial Systems,” IEEE Access, vol. 10, pp. 54567–54579, 2022.
[10] S. Kumar and R. Sharma, “Industrial IoT-Based Predictive Maintenance Framework Using Deep Learning,” Sensors, vol. 23, no. 7, pp. 3210–3225, 2023.
[2] J. Lee, B. Bagheri, and H. A. Kao, “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.
[3] S. Yin and O. Kaynak, “Big Data for Modern Industry: Challenges and Trends,” Proceedings of the IEEE, vol. 103, no. 2, pp. 143–146, 2015.
[4] A. Carvalho, E. N. S. Ferreira, and P. P. C. C. Nunes, “Machine Learning Methods Applied to Predictive Maintenance,” Computers & Industrial Engineering, vol. 137, pp. 106024, 2019.
[5] F. Tao and M. Zhang, “Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing,” IEEE Access, vol. 5, pp. 20418–20427, 2017.
[6] S. Wan, J. Lu, and P. Fan, “Edge Computing for Industrial IoT Applications,” IEEE Network, vol. 32, no. 1, pp. 58–65, 2018.
[7] A. Kusiak, “Smart Manufacturing and Predictive Maintenance: Future Trends,” Journal of Manufacturing Systems, vol. 49, pp. 136–145, 2018.
[8] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable and Undesirable Emergent Behavior in Complex Systems,” Springer, 2017.
[9] J. Chen, Y. Wang, and X. Li, “Artificial Intelligence Driven Predictive Maintenance for Industrial Systems,” IEEE Access, vol. 10, pp. 54567–54579, 2022.
[10] S. Kumar and R. Sharma, “Industrial IoT-Based Predictive Maintenance Framework Using Deep Learning,” Sensors, vol. 23, no. 7, pp. 3210–3225, 2023.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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